This invention relates generally to systems, methods, apparatuses for digital signal detection and/or decoding, and more particularly, to generating likelihood metrics for trellis-based detection and/or decoding.
With continuing demand for high-speed digital communications systems and high-density digital storage systems, various techniques have been applied to increase the capacity of these systems. For example, in magnetic media storage, many manufacturers are using perpendicular recording, rather than traditional longitudinal recording, to pack more information into a smaller area. However, as data speeds and storage densities are pushed to their limits, the amount of signal distortion on information-carrying signals have increased dramatically and detectors/decoders are heavily relied upon to interpret the information in these distorted signals.
Various sources of noise, such as thermal noise interference, and media noise arising from sources such as, jitter, may distort accurate information in digital communication and storage systems. In addition to distortions from these sources, communication and storage systems may be affected by amplitude defects, which may change the magnitude to the communicated signals and introduce further ambiguity in the detection/decoding of these signals.
Detectors and decoders may account for these ambiguities by producing reliability metrics or likelihood metrics for the detected or decoded information. These metrics may, in turn, be used by post-detection or decoding systems to process the detected or decoded information. However, known computations of likelihood metrics are typically only effective when errors are contained within code words or predetermined error update windows. As a result, they may fail to accurately predict the reliability of the detection when the communication channel is affected by certain types of defects that may cause errors that span multiple code words or multiple such predetermined error update windows.
In particular, known computations of likelihood metrics in trellis-based decoding or detection may rely solely on the metrics associated with trellis paths that converge to the same final state or bit as the decoded sequence. Therefore, metrics associated with potentially accurate sequences that fail to converge to the same final state as the decoded sequence, often due to distortions resulting from severe defects in a communication or storage channel, are ignored when computing the likelihood metric for the decoded sequence. In practice, this has an effect of producing likelihood metrics that overestimate the reliability the detection, especially when a majority of the bits in the channel are defective. Furthermore, in some scenarios, unreliable likelihood metrics may lead to catastrophic errors in the communication or storage system if, for example, they cause a subsequent processing or decoding stage to utilize faulty sequences that may have been discarded or processed differently given a more accurate likelihood metric. Therefore, it is desirable to provide techniques for providing more accurate likelihood metrics for trellis-based decoding or detection.